6 results about "Auto segmentation" patented technology

This invention discloses a medical image segmentation method and system based on local Moran's index residual blocks, belonging to the field of intelligent medical image segmentation technology. By introducing multi-scale local Moran's index features, this invention effectively quantifies the spatial clustering patterns of key anatomical structures in shoulder MRI images, achieving accurate characterization of eight types of structures. A residual Moran's feature module is deeply embedded in the UNet architecture, and spatial feature utilization is enhanced at each level of the encoder-decoder, improving the model's adaptability and generalization ability to complex shoulder anatomy, making the segmentation results more robust and reliable in clinical applications. The constructed segmentation model can achieve end-to-end automatic segmentation of shoulder MRI images without additional spatial statistical preprocessing, offering advantages such as convenient deployment and high computational efficiency. It is suitable for rapid clinical diagnosis and surgical planning, and has significant application value in improving the intelligent diagnosis and treatment of shoulder joint diseases.

This invention discloses an automatic identification method for pelvic organ prolapse based on deep learning and the PCL line, comprising: S1: acquiring and preprocessing medical images of the pelvic floor region, and simultaneously annotating the bladder, uterus, rectum, and bony anatomical structures to construct a training dataset; S2: constructing and training a multi-objective deep learning segmentation model based on the training data to achieve automatic segmentation of the bladder, uterus, rectum, pubic symphysis, and the two vertebrae from the coccyx; S3: extracting the lowest edge point of the segmented pubic symphysis region and the highest edge point of the two vertebrae from the coccyx region to construct a set of bony key points; S4: connecting the bony key points to automatically generate the pelvic floor reference line (PCL line); S5: analyzing the spatial positional relationship of the bladder, uterus, and rectum relative to the PCL line to obtain the downward displacement feature information of the pelvic organs; S6: automatically determining the degree of pelvic organ prolapse based on the spatial positional relationship. This invention achieves automated identification of the degree of pelvic organ prolapse, reduces subjective errors caused by manual measurement, and improves the consistency, accuracy, and efficiency of the assessment.

The application discloses a kind of based on real virtual constraint environment and biomechanics analysis's impacted wisdom tooth minimally invasive extraction intelligent surgical planning method and system, belong to oral medical computer-aided surgery technical field, including: obtaining the oral cavity data of patient oral cavity maxillofacial part, and utilize deep learning model to carry out automatic segmentation to oral cavity data, generate oral cavity maxillofacial three-dimensional model;Anatomical constraint parameters are extracted based on oral cavity maxillofacial three-dimensional model;Based on anatomical constraint parameters and operating space constraint parameters, the optimal global tooth extraction strategy is determined by strategy decision model;According to the global tooth extraction strategy, path planning is carried out, and the optimal extraction path for the whole or each tooth body segment after tooth is divided is generated;The optimal extraction path is converted into robot executable operation instruction sequence.Through three-dimensional modeling, anatomical constraint extraction, strategy decision and path planning, the intelligent surgical planning of impacted wisdom tooth minimally invasive extraction is realized.

The application relates to the field of artificial intelligence medical image processing technology and discloses a three-dimensional intelligent jaw bone absorption rate dynamic change monitoring method based on CBCT. CBCT data is used for three-dimensional image analysis, a deep learning model is used to automatically segment teeth, tooth enamel, maxillary bones and mandibular bones, the enamel-dentin junction and the top base of periodontal bone defects are automatically identified, the position of tooth root tips is automatically located, and the local bone absorption rate is calculated.